## An object of class Seurat
## 51215 features across 3162 samples within 3 assays
## Active assay: SCT (14612 features, 2000 variable features)
## 3 layers present: counts, data, scale.data
## 2 other assays present: RNA, HTO
## 2 dimensional reductions calculated: pca, umap
Here’re some representation
I’ll inspect the expression of some markers to determine the cell types
This will help us determine any cluster that may not be of interest ! (clusters at res 0.1)
Basically here, we’re looking for
As for the feature plots :
Now we’ll inspect the distribution of the clonotypes
Let’s group unique and low-count clones together
Now let’s see the distribution among the conditions and the clusters
Now let’s filter unwanted cells
# define cells to remove :
# ---------------------- :
cells_rm <- WhichCells(
SeuratObj,
expression = umap_1 > 6 | umap_2 > 4
)
# subset seurat object :
# -------------------- :
SeuratObj_filt <- subset(SeuratObj, cells = setdiff(Cells(SeuratObj),cells_rm))
## second round :
## ------------ :
cells_rm <- WhichCells(
SeuratObj_filt,
expression = SCT_snn_res.0.1 == "3"
)
SeuratObj_filt <- subset(SeuratObj_filt, cells = setdiff(Cells(SeuratObj_filt),cells_rm))Set a threshold of 30 cells minimum to be kept for further analysis (so any cluster below 30 will be filtered again)
Renaming clusters
Now, let’s inspect the VDJ expression :
Now, let’s inspect the cell cycle of the TCRs :
Now, let’s inspect the cell cycle of the clonotypes :
Analysis duration : 45s
Time : 13:14:25
## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
## version R version 4.4.2 (2024-10-31 ucrt)
## os Windows 11 x64 (build 26100)
## system x86_64, mingw32
## ui RTerm
## language (EN)
## collate French_France.utf8
## ctype French_France.utf8
## tz Europe/Paris
## date 2025-07-26
## pandoc 3.2 @ C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown)
## quarto NA @ C:\\PROGRA~1\\RStudio\\RESOUR~1\\app\\bin\\quarto\\bin\\quarto.exe
##
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